U.S. patent number 11,064,322 [Application Number 16/681,191] was granted by the patent office on 2021-07-13 for method, apparatus, and system for detecting joint motion.
This patent grant is currently assigned to HERE Global B.V.. The grantee listed for this patent is HERE Global B.V.. Invention is credited to Harel Primack, Herman Ravkin, Ofri Rom, Daniel Schmidt, Natalia Skorokhod, Silviu Zilberman.
United States Patent |
11,064,322 |
Zilberman , et al. |
July 13, 2021 |
Method, apparatus, and system for detecting joint motion
Abstract
An approach is provided for detecting joint motion using
multiple sensor data. The approach, for example, involves
retrieving sensor data at least two devices. The sensor data, for
instance, is collected using at least one sensor type from among a
plurality of sensor types and wherein, and each sensor type of the
plurality of sensor types is associated with a respective joint
motion classifier. The approach also involves processing the sensor
data using the respective joint motion classifier for said each
sensor type of the least one sensor type to compute a respective
sensor-type joint motion prediction. The approach further involves
processing the respective sensor-type joint motion prediction for
said each sensor type using a unified classifier to compute a
unified joint motion prediction for the at least two devices. The
approach further involves providing the unified joint motion
prediction as an output.
Inventors: |
Zilberman; Silviu (Rishon
Le-Zion, IL), Ravkin; Herman (Beer Yakov,
IL), Schmidt; Daniel (Raanana, IL),
Primack; Harel (Rishon Le-Zion, IL), Skorokhod;
Natalia (Givat-Shmuel, IL), Rom; Ofri (Ganey
Tiqwa, IL) |
Applicant: |
Name |
City |
State |
Country |
Type |
HERE Global B.V. |
Eindhoven |
N/A |
NL |
|
|
Assignee: |
HERE Global B.V. (Eindhoven,
NL)
|
Family
ID: |
1000005671478 |
Appl.
No.: |
16/681,191 |
Filed: |
November 12, 2019 |
Prior Publication Data
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|
|
Document
Identifier |
Publication Date |
|
US 20210144526 A1 |
May 13, 2021 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04B
5/0031 (20130101); H04W 4/027 (20130101); H04L
67/12 (20130101); H04W 4/80 (20180201); H04W
4/40 (20180201); H04W 4/023 (20130101); H04W
4/029 (20180201); H04W 64/006 (20130101) |
Current International
Class: |
H04B
5/00 (20060101); H04W 64/00 (20090101); H04W
4/80 (20180101); H04L 29/08 (20060101); H04W
4/40 (20180101); H04W 4/02 (20180101); H04W
4/029 (20180101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2012143301 |
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Oct 2012 |
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WO |
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2016205150 |
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Dec 2016 |
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WO |
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Other References
Carreras et al., "Comm2sense: Detecting Proximity Through
Smartphones", Article, Mar. 2012, retrieved from
https://www.researchgate.net/profile/Venet_Osmani/publication/241630493_C-
omm2Sense_Detecting_proximity_through_smartphones/links/00463535af8552a16a-
000000/Comm2Sense-Detecting-proximity-through-smartphones.pdf?origin=publi-
cation_detail, 6 pages. cited by applicant .
Office Action for related European Patent Application No.
20207288.0-1001, dated Mar. 18, 2021, 7 pages. cited by
applicant.
|
Primary Examiner: Nealon; William
Attorney, Agent or Firm: Ditthavong, Steiner &
Mlotkowski
Claims
What is claimed is:
1. A method for detecting a joint motion based on multiple sensor
data comprising: retrieving sensor data from at least two devices,
wherein the sensor data is collected using at least one sensor type
from among a plurality of sensor types and wherein each sensor type
of the plurality of sensor types is associated with a respective
joint motion classifier, wherein the plurality of different sensor
types includes a location sensor type that collects location data
as the sensor data, wherein the location data includes a distance
that the at least two devices are apart over a period of time;
processing the sensor data using the respective joint motion
classifier for said each sensor type of the at least one sensor
type to compute a respective sensor-type joint motion prediction;
processing the respective sensor-type joint motion prediction for
said each sensor type using a unified classifier to compute a
unified joint motion prediction for the at least two devices; and
providing the unified joint motion prediction as an output, wherein
the respective joint motion classifier is associated with one
sensor type of a single device.
2. The method of claim 1, wherein the unified joint motion
prediction indicates that the at least two devices are sharing a
same transportation vehicle.
3. The method of claim 1, further comprising: calculating one or
more instantaneous joint motion probabilities for the at least two
devices based on the location data; and calculating a rolling
window joint motion probability for a time window based on the one
or more instantaneous joint motion probabilities falling within the
time window, wherein the respective sensor-type joint motion
prediction for the location sensor type is based on the rolling
window joint motion probability, the one or more instantaneous
joint motion probabilities, or a combination thereof.
4. The method of claim 1, wherein the plurality of different sensor
types includes an inertial measurement unit (IMU) sensor type that
collects IMU data as the sensor data, the method further
comprising: calculating a cross-correlation between the IMU data of
the at least two devices, wherein the respective sensor-type joint
motion prediction for the IMU sensor type is based on the
cross-correlation.
5. The method of claim 1, wherein the plurality of different sensor
types includes an inertial measurement unit (IMU) sensor type that
collects IMU data as the sensor data, the method further
comprising: calculating a magnetic field for each of the at least
two devices based on the IMU data, wherein the respective
sensor-type joint motion prediction for the IMU sensor type is
based on a comparison of the magnetic field for said each of the at
least two devices.
6. The method of claim 1, wherein the plurality of different sensor
types includes a near field communication (NFC) sensor type, and
wherein the respective sensor-type joint motion prediction for the
NFC sensor type is based on determining that the at least two
devices are communicating with a same NFC device.
7. The method of claim 1, wherein the plurality of different sensor
types includes a Bluetooth sensor type that collects Bluetooth
data, the method further comprising: calculating a relative
distance between the at least two devices based on the Bluetooth
data, wherein the respective sensor-type joint motion prediction
for the Bluetooth sensor type is based on the relative
distance.
8. The method of claim 1, wherein the plurality of different sensor
types includes an acoustic sensor type that collects acoustic data,
the method further comprising: generating an acoustic signal that
is broadcast by at least one of the at least two devices, another
acoustic device, or a combination thereof that is sampled by an
acoustic sensor of the at least one of the at least two devices;
processing the acoustic data to determine a cross-correlation of
the acoustic signal between the at least two devices, wherein the
respective sensor-type joint motion prediction for the acoustic
sensor type is based on the cross-correlation.
9. The method of claim 1, wherein the plurality of different sensor
types includes an acoustic sensor type that collects acoustic data,
the method further comprising: processing the acoustic data to
determine an ambient acoustic environment of the at least two
devices, wherein the respective sensor-type joint motion prediction
for the acoustic sensor type is based on a comparison of the
ambient acoustic environment of the at least two devices.
10. The method of claim 1, wherein the plurality of different
sensor types includes a barometric sensor type that collects
barometric data as the sensor data, the method further comprising:
calculating a cross-correlation between the barometric data of the
at least two devices, wherein the respective sensor-type joint
motion prediction for the barometer sensor type is based on the
cross-correlation.
11. The method of claim 1, further comprising: retrieving map data
associated with one or more locations of the at least two devices;
calculating a map-based joint motion prediction based on the map
data, wherein the unified joint motion prediction is further based
on the map-based joint motion prediction.
12. The method of claim 1, wherein the unified classifier is a
state machine that fuses the respective sensor-type joint motion
prediction for said each sensor to compute the unified joint motion
prediction.
13. An apparatus for detecting a joint motion based on multiple
sensor data comprising: at least one processor; and at least one
memory including computer program code for one or more programs,
the at least one memory and the computer program code configured
to, with the at least one processor, cause the apparatus to perform
at least the following, retrieve sensor data from at least two
devices, wherein the sensor data is collected using at least one
sensor type from among a plurality of sensor types and wherein each
sensor type of the plurality of sensor types is associated with a
respective joint motion classifier, wherein the plurality of
different sensor types includes a location sensor type that
collects location data as the sensor data, wherein the location
data includes a distance that the at least two devices are apart
over a period of time; process the sensor data using the respective
joint motion classifier for said each sensor type of the at least
one sensor type to compute a respective sensor-type joint motion
prediction; process the respective sensor-type joint motion
prediction for said each sensor type using a unified classifier to
compute a unified joint motion prediction for the at least two
devices; and provide the unified joint motion prediction as an
output.
14. The apparatus of claim 13, wherein the unified classifier is a
state machine that fuses the respective sensor-type joint motion
prediction for said each sensor to compute the unified joint motion
prediction.
15. The apparatus of claim 13, wherein the plurality of different
sensor types includes a location sensor type, an inertial
measurement unit sensor type, a near field communication sensor
type, a Bluetooth sensor type, an acoustic sensor type, a barometer
sensor type, or a combination thereof.
16. A non-transitory computer readable storage medium for detecting
a joint motion based on multiple sensor data carrying one or more
sequences of one or more instructions which, when executed by one
or more processors, cause an apparatus to perform: retrieving
sensor data from at least two devices, wherein the sensor data is
collected using at least one sensor type from among a plurality of
sensor types and wherein each sensor type of the plurality of
sensor types is associated with a respective joint motion
classifier, wherein the plurality of different sensor types
includes a location sensor type that collects location data as the
sensor data, wherein the location data includes a distance that the
at least two devices are apart over a period of time; processing
the sensor data using the respective joint motion classifier for
said each sensor type of the at least one sensor type to compute a
respective sensor-type joint motion prediction; processing the
respective sensor-type joint motion prediction for said each sensor
type using a unified classifier to compute a unified joint motion
prediction for the at least two devices; and providing the unified
joint motion prediction as an output.
17. The non-transitory computer readable storage medium of claim
16, wherein the unified joint motion prediction indicates that the
at least two devices are sharing a same transportation vehicle.
18. The non-transitory computer readable storage medium of claim
16, wherein the unified classifier is a state machine that fuses
the respective sensor-type joint motion prediction for said each
sensor to compute the unified joint motion prediction.
19. The non-transitory computer readable storage medium of claim
16, wherein the plurality of different sensor types includes a
location sensor type, an inertial measurement unit sensor type, a
near field communication sensor type, a Bluetooth sensor type, an
acoustic sensor type, a barometer sensor type, or a combination
thereof.
20. The method of claim 1, wherein the respective sensor-type joint
motion prediction is associated with one sensor type; and the
unified joint motion prediction is associated with at least one
sensor type of the at least two devices.
Description
BACKGROUND
Service providers are continually challenged to provide new and
compelling services. One area of development relates to developing
services based on detecting whether two or more people are
traveling together in the same transportation vehicle (e.g., car,
bus, train, etc.). Accordingly, service providers face significant
technical challenges to enable automatic and accurate detection of
joint motion (e.g., traveling together in the same vehicle) between
two or more people or devices to support these services.
SOME EXAMPLE EMBODIMENTS
Therefore, there is a need for an approach for detecting joint
motion using sensor data collected from mobile devices.
According to one embodiment, a method for detecting a joint motion
based on multiple sensor data comprises retrieving sensor data from
a plurality of different sensor types of at least two devices. Each
sensor type of the plurality of sensor types, for instance, is
associated with a respective joint motion classifier. The method
also comprises processing the sensor data using the respective
joint motion classifier for said each sensor type to compute a
respective sensor-type joint motion prediction. The method further
comprises processing the respective sensor-type joint motion
prediction for said each sensor using a unified classifier to
compute a unified joint motion prediction for the at least two
devices. The method further comprises providing the unified joint
motion prediction as an output.
According to another embodiment, an apparatus for detecting a joint
motion based on multiple sensor data comprises at least one
processor, and at least one memory including computer program code
for one or more computer programs, the at least one memory and the
computer program code configured to, with the at least one
processor, cause, at least in part, the apparatus to retrieve
sensor data from a plurality of different sensor types of at least
two devices. Each sensor type of the plurality of sensor types, for
instance, is associated with a respective joint motion classifier.
The apparatus is also caused to process the sensor data using the
respective joint motion classifier for said each sensor type to
compute a respective sensor-type joint motion prediction. The
apparatus is further caused to process the respective sensor-type
joint motion prediction for said each sensor using a unified
classifier to compute a unified joint motion prediction for the at
least two devices. The apparatus is further caused to provide the
unified joint motion prediction as an output.
According to another embodiment, a computer-readable storage medium
for detecting a joint motion based on multiple sensor data carries
one or more sequences of one or more instructions which, when
executed by one or more processors, cause, at least in part, an
apparatus to retrieve sensor data from a plurality of different
sensor types of at least two devices. Each sensor type of the
plurality of sensor types, for instance, is associated with a
respective joint motion classifier. The apparatus is also caused to
process the sensor data using the respective joint motion
classifier for said each sensor type to compute a respective
sensor-type joint motion prediction. The apparatus is further
caused to process the respective sensor-type joint motion
prediction for said each sensor using a unified classifier to
compute a unified joint motion prediction for the at least two
devices. The apparatus is further caused to provide the unified
joint motion prediction as an output.
According to another embodiment, an apparatus for detecting a joint
motion based on multiple sensor data comprises means for retrieving
sensor data from a plurality of different sensor types of at least
two devices. Each sensor type of the plurality of sensor types, for
instance, is associated with a respective joint motion classifier.
The apparatus also comprises means for processing the sensor data
using the respective joint motion classifier for said each sensor
type to compute a respective sensor-type joint motion prediction.
The apparatus further comprises means for processing the respective
sensor-type joint motion prediction for said each sensor using a
unified classifier to compute a unified joint motion prediction for
the at least two devices. The apparatus further comprises means for
providing the unified joint motion prediction as an output.
According to another embodiment, the method, the apparatus, or the
computer-readable storage can be applied by one or more of the at
least two devices to detect the joint motion between the at least
two devices. In other words, a first device of the at least two
devices can perform the embodiments described herein to detect
joint motion alone or in combination with a second device of the at
least two devices, a cloud component, or a combination thereof.
In addition, for various example embodiments of the invention, the
following is applicable: a method comprising facilitating a
processing of and/or processing (1) data and/or (2) information
and/or (3) at least one signal, the (1) data and/or (2) information
and/or (3) at least one signal based, at least in part, on (or
derived at least in part from) any one or any combination of
methods (or processes) disclosed in this application as relevant to
any embodiment of the invention.
For various example embodiments of the invention, the following is
also applicable: a method comprising facilitating access to at
least one interface configured to allow access to at least one
service, the at least one service configured to perform any one or
any combination of network or service provider methods (or
processes) disclosed in this application.
For various example embodiments of the invention, the following is
also applicable: a method comprising facilitating creating and/or
facilitating modifying (1) at least one device user interface
element and/or (2) at least one device user interface
functionality, the (1) at least one device user interface element
and/or (2) at least one device user interface functionality based,
at least in part, on data and/or information resulting from one or
any combination of methods or processes disclosed in this
application as relevant to any embodiment of the invention, and/or
at least one signal resulting from one or any combination of
methods (or processes) disclosed in this application as relevant to
any embodiment of the invention.
For various example embodiments of the invention, the following is
also applicable: a method comprising creating and/or modifying (1)
at least one device user interface element and/or (2) at least one
device user interface functionality, the (1) at least one device
user interface element and/or (2) at least one device user
interface functionality based at least in part on data and/or
information resulting from one or any combination of methods (or
processes) disclosed in this application as relevant to any
embodiment of the invention, and/or at least one signal resulting
from one or any combination of methods (or processes) disclosed in
this application as relevant to any embodiment of the
invention.
In various example embodiments, the methods (or processes) can be
accomplished on the service provider side or on the mobile device
side or in any shared way between service provider and mobile
device with actions being performed on both sides.
For various example embodiments, the following is applicable: An
apparatus comprising means for performing a method of any of the
claims.
Still other aspects, features, and advantages of the invention are
readily apparent from the following detailed description, simply by
illustrating a number of particular embodiments and
implementations, including the best mode contemplated for carrying
out the invention. The invention is also capable of other and
different embodiments, and its several details can be modified in
various obvious respects, all without departing from the spirit and
scope of the invention. Accordingly, the drawings and description
are to be regarded as illustrative in nature, and not as
restrictive.
BRIEF DESCRIPTION OF THE DRAWINGS
The embodiments of the invention are illustrated by way of example,
and not by way of limitation, in the figures of the accompanying
drawings:
FIG. 1 is a diagram of a system capable of detecting joint motion
using multiple sensor data, according to one embodiment;
FIGS. 2A and 2B are diagrams illustrating examples of a joint
motion and separate motion respectively, according to one
embodiment;
FIG. 3 is table illustrating examples of computing a unified joint
motion prediction from sensor-specific joint motion predictions,
according to one embodiment;
FIG. 4 is a diagram of components of a joint motion platform,
according to one embodiment;
FIG. 5 is a flowchart of a process for detecting joint motion using
multiple sensor data, according to one embodiment;
FIGS. 6A-6D are graphs illustrating examples of location-based
joint motion predictions for use in computing a unified joint
motion prediction;
FIGS. 7A and 7B are graphs illustrating example correlations of
sensor-specific joint predictions for use in computing a unified
joint motion prediction;
FIG. 8 illustrates an example user interface for presenting a
unified joint motion prediction based on multiple sensor data,
according to one embodiment;
FIG. 9 is a diagram of a geographic database, according to one
embodiment;
FIG. 10 is a diagram of hardware that can be used to implement an
embodiment;
FIG. 11 is a diagram of a chip set that can be used to implement an
embodiment; and
FIG. 12 is a diagram of a mobile terminal that can be used to
implement an embodiment.
DESCRIPTION OF SOME EMBODIMENTS
Examples of a method, apparatus, and computer program for detecting
joint motion using multiple sensor data are disclosed. In the
following description, for the purposes of explanation, numerous
specific details are set forth in order to provide a thorough
understanding of the embodiments of the invention. It is apparent,
however, to one skilled in the art that the embodiments of the
invention may be practiced without these specific details or with
an equivalent arrangement. In other instances, well-known
structures and devices are shown in block diagram form in order to
avoid unnecessarily obscuring the embodiments of the invention.
FIG. 1 is a diagram of a system capable of detecting joint motion
using multiple sensor data, according to one embodiment. As
discussed above, automatically detecting when two or more people or
devices (e.g., user equipment (UE) devices 101a-101n--also referred
to as UEs 101--such as phones, tablets, computers, etc. with
internet connectivity) are sharing the same transportation vehicle
103 (e.g., car, bus, train, airplane, etc.). This state of sharing
or otherwise being in the same transportation vehicle 103 is
referred to herein as joint motion (JM) between the two people or
UEs 101.
One approach to determining JM or joint path identification is
based on location data only (e.g., obtained from location sensors
such as but not limited to Global Positioning System (GPS)
receivers). However, in many cases location data is not available
or inaccurate due to poor reception (e.g. disturbances of the GPS
signal from buildings, tunnels, etc.), and JM or proximity is not
detected ("false negative"). Moreover, location-based approaches
generally cannot account for "false positive" situations. One
example of a false positive situation is illustrated in FIGS. 2A
and 2B. As shown in example 200 of FIG. 2A, a location data only
approach may detect a false positive situation where it determines
that two devices (e.g., UEs 101a and UE 101b) are in the same
vehicle 201 when instead the true situation is illustrated in
example 220 of FIG. 2B where the two UEs 101a and 101b are
traveling in different respective vehicles 221a and 221b.
This is because location data only approaches typically compare how
closely the location paths of the UEs 101a and 101b are located
with respect to each other to determine JM. However, the location
data only may not be able to distinguish between the situations of
FIG. 2A and FIG. 2B under some circumstances such as where two cars
are driving nearby for extended periods of time, as occurring, for
instance, in traffic jams or when two cars are following each other
closely. In addition, due to the limited accuracy of location
information in mobile devices (e.g., typically 10-20 m in good
conditions), location data only approaches may falsely detect
nearby people or devices as potential JM. As a result, service
providers face significant technical challenges to more accurately
detecting JM states between people or devices.
To address these technical challenges, the system 100 of FIG. 1
introduces a capability to determine whether two or more people or
devices (e.g., UEs 101) are sharing the same transportation vehicle
103 (e.g., car, bus, train, airplane, boat, etc.), based on
information extracted from the sensing capabilities external to or
separate from traditional positioning sensors (e.g., Global
Positioning System (GPS), Global Navigation Satellite System
(GNSS), Inertial Measurement Unit (IMU), and/or the like) of mobile
devices, such as but not limited to phones and tablets with
internet connectivity (e.g., UEs 101 equipped with multiple sensors
105a-105m of different types that sensed different physical
characteristics). In one embodiment, the system 100 makes use of
several different types of sensors 105, typically found in mobile
devices, to collect sensor data 107 and then make individual JM
predictions for each sensor type. The individual JM predictions can
then be combined by the JM platform 109 to make a unified JM
prediction to provide an effective solution in discovering JM
behavior. The unified JM predictions or detected JM behaviors can
then be output or stored as JM data 111.
The combination of predictions made from different types of sensor
data streams provides for several advantages including but not
limited to: (1) JM can be determined even in the absence of
location data and/or inertial measurement unit (IMU) data; and (2)
ability to distinguish between true JM and multiple people or
devices moving separately nearby to one another, e.g., like in
traffic jams.
In one embodiment, the system 100 uses different JM classifiers
optimized for each sensor data type to make individual JM
predictions (e.g., individual with respect to each different sensor
type). Optimized, for instance, refers to creating or training a
classifier (e.g., in the case of machine learning based
classifiers) to recognize or take advantage of features or
algorithms specific to each type of sensor data being used. A
unified classifier (e.g., a state machine, voting algorithm, and/or
the like) controls the different sensor-specific JM classifiers and
integrates the data (e.g., individual JM predictions) from the
available sensor-specific classifiers into a single result. In one
embodiment, the JM classifiers used by the system 100 can include
JM classifiers based on location data and/or IMU data if, e.g.,
GPS/GNSS and/or IMU data are available. When such GPS/GNSS and/or
IMU are not available (e.g., when operating in areas with
interference such as urban canyons, indoor environments, etc.), the
system 100 can use JM classifiers that input sensor data from
sensors 105 other than GPS/GNSS, IMU, and/or any other type of
sensors 105 that provide location or motion as an output value.
The system 100 can integrate any number of sensor-specific JM
classifiers including but not limited to any combination of two or
more of the components described below: Location data-based
classifier--Using location data derived from GPS/GNSS, cellular
towers, WIFI, and/or equivalent location sensors. The underlying
assumption here is that close-proximity between two UEs 101 for an
extended period (e.g., a period longer threshold duration) is a
characteristic of JM. In one embodiment, a variant of the
location-based classifier for JM comprises using partial location
raw data which do not allow for location determination or for
accurate location determination, but still suffice to determine JM
accurately. IMU based classifier--Using data from accelerometers,
gyroscopes, magnetometers, and/or equivalent IMU sensors. IMU
sensor data of two devices moving together is naturally correlated
for the duration of their joint journey. Barometer based
classifier--To be used similarly to IMU data except with data
collected from barometric sensors. Close correlation barometric
data between two UEs 101 for an extended period (e.g., a period
longer than a threshold duration) is a characteristic of JM.
Bluetooth based classifier--Two Bluetooth equipped UEs 101 sensing
the received signal strength indication (RSSI) of the other
Bluetooth device are very likely to be in nearby ("active BT").
Therefore, close-proximity between two UEs 101 for an extended
period (e.g., a period longer than a threshold duration) is a
characteristic of JM. In one embodiment, a variant of the Bluetooth
based classified is based on two Bluetooth equipped UEs 101 sensing
concurrently a third transmitting Bluetooth device ("passive BT").
Near Field Communication (NFC) based classifier--Using data on
nearby NFC devices. Given two UEs 101 that can communicate via NFC,
the NFC based classifier that the NFC communication connection
indicates a proximity of the two UEs 101 of up to 20 cm or other
specified NFC range. Close proximity between two UEs 101 for an
extended period (e.g., a period longer than a threshold duration)
is a characteristic of JM. Acoustics based classifier--Using active
or passive acoustics data to identify two UEs 101 in near
proximity. Close proximity between two UEs 101 for an extended
period (e.g., a period longer than a threshold duration) is a
characteristic of JM. Maps based meta data classifier--Using
map-based data (e.g., as stored in the geographic database 113) can
significantly increase prediction quality by providing boundaries
on limitations against which JM predictions can be checked. For
example, since pickup and drop-off events are unlikely in certain
places like highways or tunnels JM motions events are unlikely to
start or stop at these locations. Therefore, if JM is detected to
start or stop at one of these locations as indicated by the
geographic database 113, the classifier can reject the
prediction.
FIG. 3 illustrates a table 300 that provides examples of computing
a unified joint motion prediction from sensor-specific joint motion
predictions, according to one embodiment. In the example of FIG. 3,
the system 100 employs six sensor-specific JM classifiers (e.g.,
location based, accelerometer correlation based, Bluetooth (BT)
based, NFC based, Acoustic based, and local magnetic field based)
that can be combined to provide a unified JM prediction. Each
classifier processes sensor data collected from respective sensor
types to make individual predictions of joint motion (e.g., "+"
indicating JM, "-" indicating separate motion, and "+/-" indicating
insufficient probability or data to make a JM prediction). FIG. 3
illustrates how the various sensor-specific classifiers complement
each other under various motion situations (e.g., some JM
situations and some separate motion situations). These various
complementary results can be used by a unified JM classifier (e.g.,
state machine, voting algorithm, machine learning classifier, or
equivalent) to make unified JM prediction based on the individual
JM results, yielding an unprecedented coverage for identifying JM
situations.
For example, in a scenario where two different UEs 101 are in
different vehicles 103 that are near each other in a traffic jam,
using location or GPS/GNSS data alone may result in a falsely
classifying the two UEs 101 as being in joint motion (i.e., moving
together in the same vehicle 103). This is because there relatively
motion may appear to be relatively consistent because the two
different vehicles 103 will be moving at approximately the same
times and locations relative to each other because they will be
stuck within the flow of the traffic jam. However, when other
sensor data (e.g., accelerometer data) is considered (alone or in
combination with the location data) for detecting joint motion
according to the embodiments described herein, a more accurate
detection can be obtained. For example, the high frequencies of the
accelerometer data obtained from the two UEs 101 will likely differ
because the magnetic fields in the two different vehicles 103 are
likely to be substantially different.
In one embodiment, the unified JM predictions (e.g., JM data 111)
can be transmitted over a communication network 115 as an output to
a services platform 117 (e.g., supporting one or more services
119a-119k--also collectively referred to as services 119) and/or
content providers 121a-121j (also collectively referred to as
content providers 121) to provide one or more potential
applications or services including but not limited to any of the
following: In paid rides (e.g. taxi fares), JM prediction can serve
as a positive identification that the ride took place and a trigger
to payment event, and in some embodiment, can also be used to
indicate falsely-cancelled rides to reduce or to prevent fraud; For
personal safety, JM prediction can be sued to verify that
passengers took the correct taxi/ride; For group safety, JM
prediction can be used to verify that in a group ride (such as
school bus or tourist bus), all individuals are in the bus and no
one is left behind; For ride sharing recommendation, JM prediction
can be used to determine the identification of people sharing the
same train, and offer them to share a taxi/shared ride for the
`last mile` issue; For taxi/shared rides, JM prediction can be used
to determine the exact location of a pickup and/or drop-off of a
passenger; For insurance, JM prediction enables insurance companies
to apply a ride-sharing insurance policy for a shared-ride when the
ride occurs and to apply a regular insurance policy when a regular
drive (i.e., non-ride-sharing trip) occurs for specific driver with
a dual use vehicle.
In one embodiment, the system 100 includes a JM platform 109 that
is capable of performing one or more functions related to detecting
JM based on multiple sensor data, according to one embodiment. As
shown in FIG. 4, the JM platform 109 includes one or more
components to perform the functions. In addition or alternatively,
in one embodiment, one or more of the UEs 101 for which joint
motion is being detected may include one or more equivalent
components to perform the JM detection functions alone or in
combination with the JM platform 109. It is contemplated that the
functions of these components may be combined or performed by other
components of equivalent functionality. In this embodiment, the JM
platform 109 includes a data ingestion module 401, two or more
sensor specific JM classifiers 403a-403n (also collectively
referred to as JM classifiers 403), unified JM classifier 405, and
output module 407. In one embodiment, the sensor specific JM
classifiers 403 and/or the unified classifier 405 can be based on
one or more machine learning models, other JM classification
algorithms, and/or equivalent (e.g., decision trees, rules, etc.)
to predict JM from corresponding sensor data 107 from associated
with respective sensor types (e.g., location sensors, IMU sensors,
BT sensors, NFC sensors, acoustic sensors, magnetometers, etc.)
and/or map meta data (e.g., queried from the geographic database
113), according to the embodiments described herein. In one
embodiment, the unified classifier 405 can be a state machine,
voting algorithm, and/or equivalent that input the individual JM
predictions of the sensor specific JM classifiers 403 to compute a
unified JM prediction, according to the embodiments described
herein. The above presented modules and components of the JM
platform 109 can be implemented in hardware, firmware, software, or
a combination thereof. Though depicted as a separate entity in FIG.
1, it is contemplated that the JM platform 109 may be implemented
as a module of any other component of the system 100 (e.g., a
component of a services platform 117, any of the services 119, UE
101, vehicle 103, etc. In another embodiment, one or more of the
modules 401-407 may be implemented as a cloud-based service, local
service, native application, or combination thereof. The functions
of the JM platform 109 and the modules 401-407 are discussed with
respect to FIGS. 5-8 below.
FIG. 5 is a flowchart of a process for detecting joint motion using
multiple sensor data, according to one embodiment. In various
embodiments, the JM platform 109 and/or any of the modules 401-407
of the JM platform 109 may perform one or more portions of the
process 500 and may be implemented in, for instance, a chip set
including a processor and a memory as shown in FIG. 11. As such,
the JM platform 109 and/or the modules 401-407 can provide means
for accomplishing various parts of the process 500, as well as
means for accomplishing embodiments of other processes described
herein in conjunction with other components of the system 100.
Although the process 500 is illustrated and described as a sequence
of steps, its contemplated that various embodiments of the process
500 may be performed in any order or combination and need not
include all of the illustrated steps.
In step 501, the data ingestion module 401 retrieves sensor data at
least two devices (e.g., UEs 101). The sensor data, for instance,
is collected from the UEs 101 using at least one sensor type from
among a plurality of sensor types available at the UEs 101. When
determining joint motion between two or more people, each UE 101
corresponds to each person (e.g., each UE 101 is a personal device
of the person). The UE 101, for instance, can be a mobile device
equipped with an array of sensors of various types (e.g., location
sensors, IMU sensors, BT sensors, NFC sensors, acoustic sensors,
etc.). In other words, a sensor type refers to a sensor configured
to measure or detect a different physical characteristic or
parameter. In one embodiment, the joint motion platform 109 is
configured with a different JM classifier, algorithm, etc. for
generating a JM motion prediction from sensor data of a particular
sensor type such that each sensor type of the plurality of sensor
types (e.g., to be processed by the JM platform 109) is associated
with a respective JM classifier 403.
In step 503, the respective JM classifier 403 processes the sensor
data using the respective joint motion classifier for said each
sensor type to compute a respective sensor-type joint motion
prediction. In other words, the sensor data stream from each type
of sensor of each of the UEs 101 is processed separately to make a
joint motion prediction based only on the corresponding sensor data
from a given sensor type to compute each sensor type joint motion
prediction. These sensor-type joint motion predictions can also be
referred to as individual joint motion predictions (i.e.,
individual to each sensor type). In one embodiment, the JM platform
109 can select a set of sensor types that are to be used to make a
unified joint motion prediction on a systemwide basis or a
case-by-case basis (e.g., depending on the capabilities or
configuration of UEs 101 associated the people being evaluated for
joint motion, on the availability of sensor data, availability of a
JM classifier, etc.). Embodiments of the example sensor-specific or
sensor type JM classifiers 403 are described below.
Location-Based JM Classifier 403a:
In one embodiment, the plurality of different sensor types
configured in the UEs 101 includes a location sensor type that
collects location data (e.g., via location sensors such as
GPS/GNSS, cellular based location, WiFi based location, etc.). By
way of example, the Location-Based JM classifier 403a operates as
follows according to one embodiment: (1) The Location-Based JM
classifier 403a assumes that the uncertainty of the position
estimation at each sample position is known. If unknown, certain
simplifying assumptions can be made, such as assuming that all
samples are derived from the same distribution. This distribution
is either known empirically or modelled by a known distribution
such as (but not limited to) a normal distribution. Without loss of
generality, the embodiment described herein illustrates but is not
limited to the case where all samples are derived from the same
normal distribution. (2) In a hypothesis-testing situation, the
null hypothesis corresponds to the two (or more devices) belonging
to random samples of a common normal distribution.
.function..mu..sigma..times..pi..times..sigma..times..function..fwdarw..m-
u..fwdarw..times..sigma. ##EQU00001## (3) The resulting distances
are positive-definite and belong to a special case of the Chi
Distribution, known as the Rayleigh Distribution in 2D or Chi with
three degrees of freedom for the 3D case. Each instantaneous
probability at each time instance t.sub.k for JM depends on the
distance d.sub.k.
.function..function..times..function. ##EQU00002##
.times..fwdarw..mu..fwdarw..sigma..times..function..fwdarw..mu..fwdarw..t-
imes..sigma. ##EQU00002.2## (4) By multiplying the k snapshots
probabilities across a time window, the Location-Based JM
classifier 403a gets a window score from the rolling
probability:
.times..times..times..times. ##EQU00003##
FIGS. 6A-6D illustrates an example use case of using the
Location-Based JM classifier 403a for location-based JM prediction.
The example use case illustrates a few points. Firstly, FIG. 6A
illustrates a subplot 600 that depicts the distance that two UEs
101 are apart over a period of time (e.g., the y-axis indicates
increasing distance from bottom to top, and the x-axis indicates
increasing time from left to right). Location sensor data is used
to determine the distance that the two UEs 101 are apart from each
other. According to the FIG. 6A, the two UEs 101 met and separated
eight times, with each "hill" in the subplot 600 corresponding to a
period of separated motion.
The subplot 620 of FIG. 6B, subplot 640 of FIG. 6C, and subplot 660
of FIG. 6D highlight various aspects of the location-based JM
prediction, according to one embodiment. For example, subplot 620
of FIG. 6B shows the instantaneous Rayleigh probability of whether
the UEs 101 are in joint motion or separate. In this example, the
probability of joint motion is on the y-axis and increases from
bottom to top, and time is on the x-axis and increase from left to
right. Subplot 620 shows some probability fluctuations resulting
from instantaneous distance calculations and the noisy signal.
Subplot 640 of FIG. 6C highlights the effect of the windowing
method described above in smoothing the plot and providing a
solution for the window. The example of subplot 640 is based on
.sigma.=20 m and T=60 s. The final subplot 660 of FIG. 6D indicates
labelled events superimposed on the decision plot with a decision
based on: P<0.1 indicating separate motion, P>0.9 indicating
joint motion, and 0.1.ltoreq.P.ltoreq.0.9 indicating undecided
motion. Darker lines correspond to separations and dashed lines to
gatherings of the two UEs 101. In this example, the Location-Based
JM classifier 403a discovered all important JM/separate motion
events within the first window of their occurrence.
In one embodiment, the Location-Based JM classifier 403a can
operate using partial location data. Partial location data, for
instance, is location data that is computed using less than a
number of independent data items for computing non-partial location
data. For example, the location data in mobile device operating
systems (e.g., Android OS) is determined, in general, by
assimilation of data taken from various location data sources
including but not limited to location satellites (e.g., GPS/GNSS
satellites), cellular network (e.g., location of cellular
towers/base stations/antennas and estimated distance), and WiFi
network (e.g., location of stations and estimated distance). For
these location methods, there generally is a minimum of concurrent
and independent data items that is needed to estimate location. For
example, data from at least four location satellites is required to
determine latitude, longitude and elevation. However, to establish
JM, fewer data items can be used, e.g., location satellites less
than the minimum of four in case of obstruction due to urban
environment. In this case, good correlation between the data
transmitted by a single satellite, as received in two or more UEs
101 (e.g., smartphones) can be indicative to JM. Therefore, in one
embodiment, the Location-Based JM classifier 403a can use partial
location data to make JM predictions.
IMU-Based JM Classifier 403b:
In one embodiment, the plurality of different sensor types
configured in the UEs 101 includes IMU sensors. Since location
provides only part of the picture, the embodiments described herein
can utilizes IMU sensors in addition or as alternate to location
sensors or other sensor types. Conceptually, the embodiments of the
IMU-Based JM classifier 403b are also applicable to any time-series
based sensor that can be used in discovering cross-correlations
between devices (e.g., barometric sensors, etc.). In one
embodiment, a cross-correlation function can be used as a measure
of similarity between two time series, acting as a function of the
displacement of one function relative to the other. The function is
also known as the sliding dot (inner) product.
By way of example, for discrete functions, the cross-correlation
can be defined as (or equivalent):
.function..times..times..infin..infin..times..function..times..function.
##EQU00004## where f, g are two discrete functions, f* is the
complex conjugate of f, and n refers to the lag between the two
functions.
Two highly correlated time series (like those that would be
expected to be seen in a JM situation) would result in a high
global maximum correlation value, located very close to zero
offset, at a small lag dt. The lag corresponds to the time
difference between the two devices (differences in their time
synchronization).
A separate motion scenario would yield a low value of maximum
correlation score, with a highly varying lag factor. In one
embodiment, for two uncorrelated time series, the maximum
correlation is as small as the noise level and its position is
random within the time window.
The IMU signals are typically noisy, due to their limited accuracy,
but also due to undesired effects such as users utilizing their
phones and causing movements unrelated to joint motion. Thus, in
one embodiment, the raw IMU signals can be preprocessed to
time-align the measurements from the two (or more) UEs 101, filter
out high frequency noise and account for motion artifacts (due to
user utilization), e.g., by incorporating gyroscope data with the
accelerometer or equivalent.
In one embodiment, there are multiple ways in which one can
construct feature vectors out of the IMU sensor data (e.g., feature
vectors for machine classification). Features may include (but are
not limited to) correlation data (e.g., maximum value, lag, decay
rate, and/or the like) for each IMU sensor (e.g., accelerometer,
gyroscope, magnetometer), amplitudes, measures of energy content in
each sensor, entropy, spectral coherence, etc. The example of FIGS.
7A and 7B illustrate a simple model based only on accelerometer
data, utilizing maximum correlation value and lag as features. The
IMU-Based JM classifier 403b calculates the correlation data in a
sliding window of several seconds in duration (e.g., typically 8-10
s). The correlation data from multiple windows (typically over a
period of 30 s to 200 s) is used to get the averaged maximum
cross-correlation and the spread of the time lag (e.g., standard
deviation as a measure). JM is expected to have high correlation
with narrow lag spread while separate motion will have low
correlation with a wide lag spread. By way of example, there are
multiple classifiers that the IMU-Based JM classifier 403b can
adopt for decision making such as but not limited to decision
trees, logistic regression variants, SVM, neural networks, etc. For
illustration purposes, FIGS. 7A and 7B demonstrate the results
obtained using logistic regression classifier.
For example, empirical analysis can demonstrate about a 95%
accuracy in 60 sec using the IMU-Based JM classifier 403b as
demonstrated in the histogram 700 of FIG. 7A and histogram 720 of
FIG. 7B. Note, this approach does not require precise clock
synchronization and works well with clock differences up to a few
seconds or more.
In one embodiment, the IMU-Based JM classifier 403b can also use
the magnetic field, as typically sampled by the IMU, to determine
or predict JM. There are a few utilization options possible. For
example, the first one consists on the unique quasi-static magnetic
field that is present within the car or vehicle 103, which is
generated by the earth's magnetic field and the corresponding
magnetic induction thereof. The spatial magnetic vector, as well as
its relatively slow change over time due to turns, can be used for
JM determination. In addition, higher frequency magnetic field
components, emerging from the electronic systems of the car or
vehicle 103, and which are typical to a vehicle model or even a
single vehicle, can be used as a "vehicle fingerprint" to determine
JM. For example, if both UEs 101 detect the same vehicle
fingerprint, the IMU-Based JM classifier 403b can determine joint
motion.
NFC-Based JM Classifier 403c:
In one embodiment, the plurality of different sensor types
configured in the UEs 101 includes NFC receivers or sensors.
Generally, NFC communication works at extremely short ranges,
typically up to 20 cm. In a JM situation, such short separations
between two phones are plausible though not very frequent (e.g., if
two UEs 101 are placed in the same compartment). If two UEs 101 can
communicate via NFC, it is an absolute indication of proximity. If
the proximity extends for more than a threshold time window (e.g.,
1 min), it indicates a JM event that can be predicted by the
NFC-Based JM Classifier 403c.
Bluetooth-Based JM Classifier 403d:
In one embodiment, the plurality of different sensor types
configured in the UEs 101 includes Bluetooth or equivalent
short-range wireless receivers or sensors. Bluetooth signal
intensity is distance-dependent and has a power-law decay as the
distance increases. For example, the distance estimation model
based on Bluetooth signals is given as:
RSSI(d)=-(10.times.n)log.sub.10 d-A Where n is the effective decay
exponent, d is the relative distance between the communicating
devices, and A is a reference received signal strength in dBm
(e.g., the RSSI value measured when the separation distance between
the receiver and the transmitter is one meter). Although the
constants of the above expression are unknown, one can get a rough
estimate based on the BT device spec, so if two UEs 101 can scan
for the RSSI signal and get a meaningful value, the Bluetooth-Based
JM classifier 403d can determine that the two UEs 101 are nearby
and can get a rough distance estimate to within a few meters.
Moreover, if the RSSI is relatively constant over an extended
period (e.g., more than a threshold duration), it is an indication
of a JM event that can be used by the Bluetooth-Based JM classifier
403d to predict JM.
In one embodiment, once the Bluetooth-Based JM classifier 403d has
an estimate of the distance between two or more UEs 101, a
methodology similar to the location-based algorithm above can be
applied to predict JM.
Acoustic-Based JM Classifier 403e:
In one embodiment, the plurality of different sensor types
configured in the UEs 101 includes acoustic sensors (e.g.,
microphones). In one embodiment, acoustics can be employed in two
modes of operation, active or passive. For example, in active mode,
one of the two or more UEs 101 (or both) can broadcast short bursts
of predefined signals via the internal speaker. The signals are
arbitrary, but the system 100 can select signals in the higher part
of the microphone frequency response, typically in the 15-22 kHz.
Examples for such acoustic signals are pseudo random noise
sequences (e.g., a maximum length sequences--MLS sequences) that
allow for high signal to noise ratio at relatively low broadcast
intensity, thus minimizing the audible inconvenience to the people
around. The microphone on the receiving UE 101 records the signal
and compares it to the (known) broadcast signal, e.g., by a time
cross-correlation function. The Acoustic-Based JM classifier 403e
can use a high peak in the cross-correlation is an indication that
two or more UEs 101 are in proximity of each other.
In the passive mode, the two UEs 101 synchronize the recording of
background noise over a few seconds or other designated period of
time. The two signals are highly correlated if the two devices are
nearby. The Acoustic-Based JM classifier 403e can then use the
correlation to predict JM.
Map-Based Meta-Data JM Classifier 403f:
In one embodiment, combining maps (e.g., the geographic database
113) and location data (e.g., sensed by the UEs 101) can provide
useful metadata. The Map-Based JM classifier 403f can use the
sensed location data of the UEs 101 to query for meta data or
attributes associated with the location of the UEs 101 that can
help increase the accuracy of JM predictions. For example, since
drop-off and pickup of passengers are unlikely to happen on
highways and tunnels, the JM events are detected at such locations
are may likely be false positive events. In another use case, for
specific individuals for which personal points of interest are
known (e.g. home, work, etc.), or global points of interest such as
shopping centers, train stations etc., there is an increased
probability that JM events will take place at such locations. In
other words, the Map-Based JM classifier 403f, retrieves map data
associated with one or more locations of the at least two UEs 101,
and calculates a map-based joint motion prediction or verifies a
joint motion prediction based on the map data. In one embodiment,
instead of being used by a separate classifier, the map metadata
can be fused in the unified classifier 405 described below.
In step 505, the unified classifier 405 processes the respective
sensor-type joint motion prediction for said each sensor to compute
a unified joint motion prediction for the at least two devices. As
previously described, the unified joint motion prediction indicates
that the at least two devices are sharing a same transportation
vehicle based on multiple different sensor types and not just one
sensor (e.g., as in the location data only approach). In one
embodiment, considering that the JM platform 109 has multiple data
sources (e.g., multiple individual sensor specific joint motion
predictions) and metadata (e.g., map data from the geographic
database 113) to fuse together, multiple approaches can be applied.
The approaches include (but are not limited to) hidden Markov
model, classifiers such as decision trees and their variants,
auto-regressive linear models, deep neural networks (DNN), and/or
equivalent.
In one embodiment, a State Machine architecture, comprised of
current and all potential states of the system and transition
functions between them, can also be constructed to compute a
unified joint motion prediction from the different data sources. In
yet another embodiment, the unified JM classifier 405 can be based
on a voting algorithm or equivalent to combine the different
sensor-specific joint motion predictions. One example voting scheme
can include but is not limited to simply taking a majority vote
among the different sensor-specific classifiers 403 to determine
whether two or more UEs 101 are in joint motion, separate motion,
and/or undecided motion.
In step 507, the output module 407 provides the unified joint
motion prediction as an output. It is contemplated output can be
used to support any service or application which relies or uses
joint motion data. In one embodiment, the output can be used
directly by the JM platform 109 or provided to external services or
applications (e.g., via the service platform 117, services 119,
content providers 121, etc.). FIG. 8 illustrates an example user
interface 800 for presenting a unified joint motion prediction
based on multiple sensor data, according to one embodiment. In the
example of FIG. 8, the JM platform 109 has ingested sensor data
from multiple sensors of two devices (e.g., device A and device B)
to determine whether the two devices are in a joint motion state.
The sensor data for instance includes data collected over a period
of time (e.g., between 10:00 AM and 10:30 AM on a given day of the
week). The JM platform 109 determines that the collected data
includes the data of the following sensor types: location, IMU,
IMU-magnetic, NFC, Bluetooth, acoustic, Barometer, and map
metadata.
The JM platform 109 then generates a user interface element 801
that presents a list of sensor types available for predicting joint
motion according and options for the user to select which of the
sensor types to use for joint motion prediction. As shown, the user
has selected the following sensor types (e.g., as indicated by a
black box next to the sensor type): location, IMU, Bluetooth,
acoustic, and map metadata. The JM platform 109 then applies the
sensor specific classifiers 403 corresponding to the selected
sensor types to make individual or sensor specific joint motion
predictions. The unified classifier 405 of the JM platform 109
fuses individual joint motion predictions with the corresponding
map metadata to make a unified joint motion prediction for the
devices A and B for the corresponding time period. The JM platform
109 then presents a user interface element 803 to display the
unified joint motion prediction (e.g., "Joint motion detected
between device A and device B between 10:00 AM and 10:30 AM") and
provides the corresponding prediction probability or confidence
(e.g., "Probability 0.95").
Returning to FIG. 1, the system 100 comprises one or more UEs 101
and/or one or more vehicles 103 having connectivity to the JM
platform 109 via a communication network 115. By way of example,
the UEs 101 may be a personal navigation device ("PND"), a cellular
telephone, a mobile phone, a personal digital assistant ("PDA"), a
watch, a camera, a computer, an in-vehicle or embedded navigation
system, and/or other device that is configured with multiple
sensors types that can be used for join motion detection according
to the embodiments described herein. It is contemplated, that the
cellular telephone or other wireless communication device may be
interfaced with an on-board navigation system of an autonomous
vehicle or physically connected to the vehicle 103 for serving as
the navigation system. Also, the UEs 101 and/or vehicles 103 may be
configured to access the communication network 115 by way of any
known or still developing communication protocols. Via this
communication network 115, the UEs 101 and/or vehicles 103 may
transmit sensor data collected from multiple different sensor types
105 for facilitating joint motion detection.
The UEs 101 and/or vehicles 103 may be configured with multiple
sensors 105 of different types for acquiring and/or generating
sensor data according to the embodiments described herein. For
example, sensors 105 may be used as GPS or other positioning
receivers for interacting with one or more location satellites to
determine and track the current speed, position and location of a
vehicle travelling along a roadway. In addition, the sensors 105
may gather IMU data, NFC data, Bluetooth data, acoustic data,
barometric data, tilt data (e.g., a degree of incline or decline of
the vehicle during travel), motion data, light data, sound data,
image data, weather data, temporal data and other data associated
with the vehicle and/or UEs 101 thereof. Still further, the sensors
105 may detect local or transient network and/or wireless signals,
such as those transmitted by nearby devices during navigation of a
vehicle along a roadway. This may include, for example, network
routers configured within a premise (e.g., home or business),
another UE 101 or vehicle 103 or a communicable traffic system
(e.g., traffic lights, traffic cameras, traffic signals, digital
signage). In one embodiment, the JM platform 109 aggregates
multiple sensor data gathered and/or generated by the UEs 101
and/or vehicles 103 resulting from traveling in joint motion or
separate motion.
By way of example, the JM platform 109 may be implemented as a
cloud based service, hosted solution or the like for performing the
above described functions. Alternatively, the JM platform 109 may
be directly integrated for processing data generated and/or
provided by the service platform 117, one or more services 119,
and/or content providers 121. Per this integration, the JM platform
109 may perform client-side state computation of road curvature
data.
By way of example, the communication network 115 of system 100
includes one or more networks such as a data network, a wireless
network, a telephony network, or any combination thereof. It is
contemplated that the data network may be any local area network
(LAN), metropolitan area network (MAN), wide area network (WAN), a
public data network (e.g., the Internet), short range wireless
network, or any other suitable packet-switched network, such as a
commercially owned, proprietary packet-switched network, e.g., a
proprietary cable or fiber-optic network, and the like, or any
combination thereof. In addition, the wireless network may be, for
example, a cellular network and may employ various technologies
including enhanced data rates for global evolution (EDGE), general
packet radio service (GPRS), global system for mobile
communications (GSM), Internet protocol multimedia subsystem (IMS),
universal mobile telecommunications system (UMTS), etc., as well as
any other suitable wireless medium, e.g., worldwide
interoperability for microwave access (WiMAX), Long Term Evolution
(LTE) networks, code division multiple access (CDMA), wideband code
division multiple access (WCDMA), wireless fidelity (WiFi),
wireless LAN (WLAN), Bluetooth.RTM., Internet Protocol (IP) data
casting, satellite, mobile ad-hoc network (MANET), and the like, or
any combination thereof.
A UE 101 is any type of mobile terminal, fixed terminal, or
portable terminal including a mobile handset, station, unit,
device, multimedia computer, multimedia tablet, Internet node,
communicator, desktop computer, laptop computer, notebook computer,
netbook computer, tablet computer, personal communication system
(PCS) device, personal navigation device, personal digital
assistants (PDAs), audio/video player, digital camera/camcorder,
positioning device, television receiver, radio broadcast receiver,
electronic book device, game device, or any combination thereof,
including the accessories and peripherals of these devices, or any
combination thereof. It is also contemplated that a UE 101 can
support any type of interface to the user (such as "wearable"
circuitry, etc.).
By way of example, the UE 101s, the JM platform 109, the service
platform 117, and the content providers 121 communicate with each
other and other components of the communication network 115 using
well known, new or still developing protocols. In this context, a
protocol includes a set of rules defining how the network nodes
within the communication network 115 interact with each other based
on information sent over the communication links. The protocols are
effective at different layers of operation within each node, from
generating and receiving physical signals of various types, to
selecting a link for transferring those signals, to the format of
information indicated by those signals, to identifying which
software application executing on a computer system sends or
receives the information. The conceptually different layers of
protocols for exchanging information over a network are described
in the Open Systems Interconnection (OSI) Reference Model.
Communications between the network nodes are typically effected by
exchanging discrete packets of data. Each packet typically
comprises (1) header information associated with a particular
protocol, and (2) payload information that follows the header
information and contains information that may be processed
independently of that particular protocol. In some protocols, the
packet includes (3) trailer information following the payload and
indicating the end of the payload information. The header includes
information such as the source of the packet, its destination, the
length of the payload, and other properties used by the protocol.
Often, the data in the payload for the particular protocol includes
a header and payload for a different protocol associated with a
different, higher layer of the OSI Reference Model. The header for
a particular protocol typically indicates a type for the next
protocol contained in its payload. The higher layer protocol is
said to be encapsulated in the lower layer protocol. The headers
included in a packet traversing multiple heterogeneous networks,
such as the Internet, typically include a physical (layer 1)
header, a data-link (layer 2) header, an internetwork (layer 3)
header and a transport (layer 4) header, and various application
(layer 5, layer 6 and layer 7) headers as defined by the OSI
Reference Model.
FIG. 9 is a diagram of a geographic database, according to one
embodiment. In one embodiment, the geographic database 113 includes
geographic data 901 used for (or configured to be compiled to be
used for) mapping and/or navigation-related services, such as for
providing map embedding analytics according to the embodiments
described herein. For example, the map data records stored herein
can be used to determine the semantic relationships among the map
features, attributes, categories, etc. represented in the
geographic data 901. In one embodiment, the geographic database 113
include high definition (HD) mapping data that provide
centimeter-level or better accuracy of map features. For example,
the geographic database 113 can be based on Light Detection and
Ranging (LiDAR) or equivalent technology to collect billions of 3D
points and model road surfaces and other map features down to the
number lanes and their widths. In one embodiment, the HD mapping
data (e.g., HD data records 911) capture and store details such as
the slope and curvature of the road, lane markings, roadside
objects such as sign posts, including what the signage denotes. By
way of example, the HD mapping data enable highly automated
vehicles to precisely localize themselves on the road.
In one embodiment, geographic features (e.g., two-dimensional or
three-dimensional features) are represented using polylines and/or
polygons (e.g., two-dimensional features) or polygon extrusions
(e.g., three-dimensional features). In one embodiment, these
polylines/polygons can also represent ground truth or reference
features or objects (e.g., signs, road markings, lane lines,
landmarks, etc.) used for visual odometry. For example, the
polylines or polygons can correspond to the boundaries or edges of
the respective geographic features. In the case of a building, a
two-dimensional polygon can be used to represent a footprint of the
building, and a three-dimensional polygon extrusion can be used to
represent the three-dimensional surfaces of the building.
Accordingly, the terms polygons and polygon extrusions as used
herein can be used interchangeably.
In one embodiment, the following terminology applies to the
representation of geographic features in the geographic database
113.
"Node"--A point that terminates a link.
"Line segment"--A straight line connecting two points.
"Link" (or "edge")--A contiguous, non-branching string of one or
more line segments terminating in a node at each end.
"Shape point"--A point along a link between two nodes (e.g., used
to alter a shape of the link without defining new nodes).
"Oriented link"--A link that has a starting node (referred to as
the "reference node") and an ending node (referred to as the "non
reference node").
"Simple polygon"--An interior area of an outer boundary formed by a
string of oriented links that begins and ends in one node. In one
embodiment, a simple polygon does not cross itself.
"Polygon"--An area bounded by an outer boundary and none or at
least one interior boundary (e.g., a hole or island). In one
embodiment, a polygon is constructed from one outer simple polygon
and none or at least one inner simple polygon. A polygon is simple
if it just consists of one simple polygon, or complex if it has at
least one inner simple polygon.
In one embodiment, the geographic database 113 follows certain
conventions. For example, links do not cross themselves and do not
cross each other except at a node. Also, there are no duplicated
shape points, nodes, or links. Two links that connect each other
have a common node. In the geographic database 113, overlapping
geographic features are represented by overlapping polygons. When
polygons overlap, the boundary of one polygon crosses the boundary
of the other polygon. In the geographic database 113, the location
at which the boundary of one polygon intersects they boundary of
another polygon is represented by a node. In one embodiment, a node
may be used to represent other locations along the boundary of a
polygon than a location at which the boundary of the polygon
intersects the boundary of another polygon. In one embodiment, a
shape point is not used to represent a point at which the boundary
of a polygon intersects the boundary of another polygon.
As shown, the geographic database 113 includes node data records
903, road segment or link data records 905, POI data records 907,
joint motion data records 909, HD mapping data records 911, and
indexes 913, for example. More, fewer or different data records can
be provided. In one embodiment, additional data records (not shown)
can include cartographic ("carto") data records, routing data, and
maneuver data. In one embodiment, the indexes 913 may improve the
speed of data retrieval operations in the geographic database 113.
In one embodiment, the indexes 913 may be used to quickly locate
data without having to search every row in the geographic database
113 every time it is accessed. For example, in one embodiment, the
indexes 913 can be a spatial index of the polygon points associated
with stored feature polygons.
In exemplary embodiments, the road segment data records 905 are
links or segments representing roads, streets, or paths, as can be
used in the calculated route or recorded route information for
determination of one or more personalized routes. The node data
records 903 are end points corresponding to the respective links or
segments of the road segment data records 905. The road link data
records 905 and the node data records 903 represent a road network,
such as used by vehicles, cars, and/or other entities.
Alternatively, the geographic database 113 can contain path segment
and node data records or other data that represent pedestrian paths
or areas in addition to or instead of the vehicle road record data,
for example. In one embodiment, the nodes and links can make up the
base map and that base map can be associated with an HD layer
including more detailed information, like lane level details for
each road segment or link and how those lanes connect via
intersections. Furthermore, another layer may also be provided,
such as an HD live map, where road objects are provided in detail
in regard to positioning, which can be used for localization. The
HD layers can be arranged in a tile format.
The road/link segments and nodes can be associated with attributes,
such as geographic coordinates, street names, address ranges, speed
limits, turn restrictions at intersections, and other navigation
related attributes, as well as POIs, such as gasoline stations,
hotels, restaurants, museums, stadiums, offices, automobile
dealerships, auto repair shops, buildings, stores, parks, etc. The
geographic database 113 can include data about the POIs and their
respective locations in the POI data records 907. The geographic
database 113 can also include data about places, such as cities,
towns, or other communities, and other geographic features, such as
bodies of water, mountain ranges, etc. Such place or feature data
can be part of the POI data records 907 or can be associated with
POIs or POI data records 907 (such as a data point used for
displaying or representing a position of a city).
In one embodiment, the geographic database 113 can also include
joint motion data records 909 for storing joint motion data
predicted from one or more people or devices. The joint motion data
records 909 can also store related data including but not limited
to underlying sensor data probe data, individual or sensor specific
joint motion detections, available sensor types, and/or any other
data used or generated according to the embodiments described
herein. By way of example, the joint motion data records 909 can be
associated with one or more of the node records 903, road segment
records 905, and/or POI data records 907 to associate the detected
joint motion with specific geographic areas or features. In this
way, the map embedding data records 909 can also be associated with
the characteristics or metadata of the corresponding records 903,
905, and/or 907.
In one embodiment, as discussed above, the HD mapping data records
911 model road surfaces and other map features to centimeter-level
or better accuracy (e.g., including centimeter-level accuracy for
ground truth objects used for visual odometry based on polyline
homogeneity according to the embodiments described herein). The HD
mapping data records 911 also include ground truth object models
that provide the precise object geometry with polylines or
polygonal boundaries, as well as rich attributes of the models.
These rich attributes include, but are not limited to, object type,
object location, lane traversal information, lane types, lane
marking types, lane level speed limit information, and/or the like.
In one embodiment, the HD mapping data records 911 are divided into
spatial partitions of varying sizes to provide HD mapping data to
end user devices with near real-time speed without overloading the
available resources of the devices (e.g., computational, memory,
bandwidth, etc. resources).
In one embodiment, the HD mapping data records 911 are created from
high-resolution 3D mesh or point-cloud data generated, for
instance, from LiDAR-equipped vehicles. The 3D mesh or point-cloud
data are processed to create 3D representations of a street or
geographic environment at centimeter-level accuracy for storage in
the HD mapping data records 911.
In one embodiment, the HD mapping data records 911 also include
real-time sensor data collected from probe vehicles in the field.
The real-time sensor data, for instance, integrates real-time
traffic information, weather, and road conditions (e.g., potholes,
road friction, road wear, etc.) with highly detailed 3D
representations of street and geographic features to provide
precise real-time data (e.g., including probe trajectories) also at
centimeter-level accuracy. Other sensor data can include vehicle
telemetry or operational data such as windshield wiper activation
state, braking state, steering angle, accelerator position, and/or
the like. The HD mapping data records may be provided as a separate
map layer.
In one embodiment, the geographic database 113 can be maintained by
the content provider 121 in association with the services platform
117 (e.g., a map developer). The map developer can collect
geographic data to generate and enhance the geographic database
113. There can be different ways used by the map developer to
collect data. These ways can include obtaining data from other
sources, such as municipalities or respective geographic
authorities. In addition, the map developer can employ field
personnel to travel by vehicle along roads throughout the
geographic region to observe features and/or record information
about them, for example. Also, remote sensing, such as aerial or
satellite photography, can be used.
The geographic database 113 can be a master geographic database
stored in a format that facilitates updating, maintenance, and
development. For example, the master geographic database or data in
the master geographic database can be in an Oracle spatial format
or other spatial format, such as for development or production
purposes. The Oracle spatial format or development/production
database can be compiled into a delivery format, such as a
geographic data files (GDF) format. Other formats including tile
structures for different map layers may be used for different
delivery techniques. The data in the production and/or delivery
formats can be compiled or further compiled to form geographic
database products or databases, which can be used in end user
navigation devices or systems.
For example, geographic data is compiled (such as into a platform
specification format (PSF)) to organize and/or configure the data
for performing navigation-related functions and/or services, such
as route calculation, route guidance, map display, speed
calculation, distance and travel time functions, and other
functions, by a navigation device, such as by a vehicle 103 and/or
UE 101. The navigation-related functions can correspond to vehicle
navigation, pedestrian navigation, or other types of navigation.
The compilation to produce the end user databases can be performed
by a party or entity separate from the map developer. For example,
a customer of the map developer, such as a navigation device
developer or other end user device developer, can perform
compilation on a received geographic database in a delivery format
to produce one or more compiled navigation databases.
The processes described herein for detecting joint motion using
multiple sensor data may be advantageously implemented via
software, hardware (e.g., general processor, Digital Signal
Processing (DSP) chip, an Application Specific Integrated Circuit
(ASIC), Field Programmable Gate Arrays (FPGAs), Quantum Computer,
etc.), firmware or a combination thereof. Such exemplary hardware
for performing the described functions is detailed below.
FIG. 10 illustrates a computer system 1000 upon which an embodiment
of the invention may be implemented. Computer system 1000 is
programmed (e.g., via computer program code or instructions) to
detect joint motion using multiple sensor data as described herein
and includes a communication mechanism such as a bus 1010 for
passing information between other internal and external components
of the computer system 1000. Information (also called data) is
represented as a physical expression of a measurable phenomenon,
typically electric voltages, but including, in other embodiments,
such phenomena as magnetic, electromagnetic, pressure, chemical,
biological, molecular, atomic, sub-atomic and quantum interactions.
For example, north and south magnetic fields, or a zero and
non-zero electric voltage, represent two states (0, 1) of a binary
digit (bit). Other phenomena can represent digits of a higher base.
A superposition of multiple simultaneous quantum states before
measurement represents a quantum bit (qubit). A sequence of one or
more digits constitutes digital data that is used to represent a
number or code for a character. In some embodiments, information
called analog data is represented by a near continuum of measurable
values within a particular range.
A bus 1010 includes one or more parallel conductors of information
so that information is transferred quickly among devices coupled to
the bus 1010. One or more processors 1002 for processing
information are coupled with the bus 1010.
A processor 1002 performs a set of operations on information as
specified by computer program code related to detecting joint
motion using multiple sensor data. The computer program code is a
set of instructions or statements providing instructions for the
operation of the processor and/or the computer system to perform
specified functions. The code, for example, may be written in a
computer programming language that is compiled into a native
instruction set of the processor. The code may also be written
directly using the native instruction set (e.g., machine language).
The set of operations include bringing information in from the bus
1010 and placing information on the bus 1010. The set of operations
also typically include comparing two or more units of information,
shifting positions of units of information, and combining two or
more units of information, such as by addition or multiplication or
logical operations like OR, exclusive OR (XOR), and AND. Each
operation of the set of operations that can be performed by the
processor is represented to the processor by information called
instructions, such as an operation code of one or more digits. A
sequence of operations to be executed by the processor 1002, such
as a sequence of operation codes, constitute processor
instructions, also called computer system instructions or, simply,
computer instructions. Processors may be implemented as mechanical,
electrical, magnetic, optical, chemical or quantum components,
among others, alone or in combination.
Computer system 1000 also includes a memory 1004 coupled to bus
1010. The memory 1004, such as a random access memory (RAM) or
other dynamic storage device, stores information including
processor instructions for detecting joint motion using multiple
sensor data. Dynamic memory allows information stored therein to be
changed by the computer system 1000. RAM allows a unit of
information stored at a location called a memory address to be
stored and retrieved independently of information at neighboring
addresses. The memory 1004 is also used by the processor 1002 to
store temporary values during execution of processor instructions.
The computer system 1000 also includes a read only memory (ROM)
1006 or other static storage device coupled to the bus 1010 for
storing static information, including instructions, that is not
changed by the computer system 1000. Some memory is composed of
volatile storage that loses the information stored thereon when
power is lost. Also coupled to bus 1010 is a non-volatile
(persistent) storage device 1008, such as a magnetic disk, optical
disk or flash card, for storing information, including
instructions, that persists even when the computer system 1000 is
turned off or otherwise loses power.
Information, including instructions for detecting joint motion
using multiple sensor data, is provided to the bus 1010 for use by
the processor from an external input device 1012, such as a
keyboard containing alphanumeric keys operated by a human user, or
a sensor. A sensor detects conditions in its vicinity and
transforms those detections into physical expression compatible
with the measurable phenomenon used to represent information in
computer system 1000. Other external devices coupled to bus 1010,
used primarily for interacting with humans, include a display
device 1014, such as a cathode ray tube (CRT) or a liquid crystal
display (LCD), or plasma screen or printer for presenting text or
images, and a pointing device 1016, such as a mouse or a trackball
or cursor direction keys, or motion sensor, for controlling a
position of a small cursor image presented on the display 1014 and
issuing commands associated with graphical elements presented on
the display 1014. In some embodiments, for example, in embodiments
in which the computer system 1000 performs all functions
automatically without human input, one or more of external input
device 1012, display device 1014 and pointing device 1016 is
omitted.
In the illustrated embodiment, special purpose hardware, such as an
application specific integrated circuit (ASIC) 1020, is coupled to
bus 1010. The special purpose hardware is configured to perform
operations not performed by processor 1002 quickly enough for
special purposes. Examples of application specific ICs include
graphics accelerator cards for generating images for display 1014,
cryptographic boards for encrypting and decrypting messages sent
over a network, speech recognition, and interfaces to special
external devices, such as robotic arms and medical scanning
equipment that repeatedly perform some complex sequence of
operations that are more efficiently implemented in hardware.
Computer system 1000 also includes one or more instances of a
communications interface 1070 coupled to bus 1010. Communication
interface 1070 provides a one-way or two-way communication coupling
to a variety of external devices that operate with their own
processors, such as printers, scanners and external disks. In
general the coupling is with a network link 1078 that is connected
to a local network 1080 to which a variety of external devices with
their own processors are connected. For example, communication
interface 1070 may be a parallel port or a serial port or a
universal serial bus (USB) port on a personal computer. In some
embodiments, communications interface 1070 is an integrated
services digital network (ISDN) card or a digital subscriber line
(DSL) card or a telephone modem that provides an information
communication connection to a corresponding type of telephone line.
In some embodiments, a communication interface 1070 is a cable
modem that converts signals on bus 1010 into signals for a
communication connection over a coaxial cable or into optical
signals for a communication connection over a fiber optic cable. As
another example, communications interface 1070 may be a local area
network (LAN) card to provide a data communication connection to a
compatible LAN, such as Ethernet. Wireless links may also be
implemented. For wireless links, the communications interface 1070
sends or receives or both sends and receives electrical, acoustic
or electromagnetic signals, including infrared and optical signals,
that carry information streams, such as digital data. For example,
in wireless handheld devices, such as mobile telephones like cell
phones, the communications interface 1070 includes a radio band
electromagnetic transmitter and receiver called a radio
transceiver. In certain embodiments, the communications interface
1070 enables connection to the communication network 115 for
detecting joint motion using multiple sensor data.
The term computer-readable medium is used herein to refer to any
medium that participates in providing information to processor
1002, including instructions for execution. Such a medium may take
many forms, including, but not limited to, non-volatile media,
volatile media and transmission media. Non-volatile media include,
for example, optical or magnetic disks, such as storage device
1008. Volatile media include, for example, dynamic memory 1004.
Transmission media include, for example, coaxial cables, copper
wire, fiber optic cables, and carrier waves that travel through
space without wires or cables, such as acoustic waves and
electromagnetic waves, including radio, optical and infrared waves.
Signals include man-made transient variations in amplitude,
frequency, phase, polarization or other physical properties
transmitted through the transmission media. Common forms of
computer-readable media include, for example, a floppy disk, a
flexible disk, hard disk, magnetic tape, any other magnetic medium,
a CD-ROM, CDRW, DVD, any other optical medium, punch cards, paper
tape, optical mark sheets, any other physical medium with patterns
of holes or other optically recognizable indicia, a RAM, a PROM, an
EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier
wave, or any other medium from which a computer can read.
FIG. 11 illustrates a chip set 1100 upon which an embodiment of the
invention may be implemented. Chip set 1100 is programmed to detect
joint motion using multiple sensor data as described herein and
includes, for instance, the processor and memory components
described with respect to FIG. 10 incorporated in one or more
physical packages (e.g., chips). By way of example, a physical
package includes an arrangement of one or more materials,
components, and/or wires on a structural assembly (e.g., a
baseboard) to provide one or more characteristics such as physical
strength, conservation of size, and/or limitation of electrical
interaction. It is contemplated that in certain embodiments the
chip set can be implemented in a single chip.
In one embodiment, the chip set 1100 includes a communication
mechanism such as a bus 1101 for passing information among the
components of the chip set 1100. A processor 1103 has connectivity
to the bus 1101 to execute instructions and process information
stored in, for example, a memory 1105. The processor 1103 may
include one or more processing cores with each core configured to
perform independently. A multi-core processor enables
multiprocessing within a single physical package. Examples of a
multi-core processor include two, four, eight, or greater numbers
of processing cores. Alternatively or in addition, the processor
1103 may include one or more microprocessors configured in tandem
via the bus 1101 to enable independent execution of instructions,
pipelining, and multithreading. The processor 1103 may also be
accompanied with one or more specialized components to perform
certain processing functions and tasks such as one or more digital
signal processors (DSP) 1107, or one or more application-specific
integrated circuits (ASIC) 1109. A DSP 1107 typically is configured
to process real-world signals (e.g., sound) in real time
independently of the processor 1103. Similarly, an ASIC 1109 can be
configured to performed specialized functions not easily performed
by a general purposed processor. Other specialized components to
aid in performing the inventive functions described herein include
one or more field programmable gate arrays (FPGA) (not shown), one
or more controllers (not shown), or one or more other
special-purpose computer chips.
The processor 1103 and accompanying components have connectivity to
the memory 1105 via the bus 1101. The memory 1105 includes both
dynamic memory (e.g., RAM, magnetic disk, writable optical disk,
etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing
executable instructions that when executed perform the inventive
steps described herein to detect joint motion using multiple sensor
data. The memory 1105 also stores the data associated with or
generated by the execution of the inventive steps.
FIG. 12 is a diagram of exemplary components of a mobile terminal
1201 (e.g., a UE 101, vehicle 103, or part thereof) capable of
operating in the system of FIG. 1, according to one embodiment.
Generally, a radio receiver is often defined in terms of front-end
and back-end characteristics. The front-end of the receiver
encompasses all of the Radio Frequency (RF) circuitry whereas the
back-end encompasses all of the base-band processing circuitry.
Pertinent internal components of the telephone include a Main
Control Unit (MCU) 1203, a Digital Signal Processor (DSP) 1205, and
a receiver/transmitter unit including a microphone gain control
unit and a speaker gain control unit. A main display unit 1207
provides a display to the user in support of various applications
and mobile station functions that offer automatic contact matching.
An audio function circuitry 1209 includes a microphone 1211 and
microphone amplifier that amplifies the speech signal output from
the microphone 1211. The amplified speech signal output from the
microphone 1211 is fed to a coder/decoder (CODEC) 1213.
A radio section 1215 amplifies power and converts frequency in
order to communicate with a base station, which is included in a
mobile communication system, via antenna 1217. The power amplifier
(PA) 1219 and the transmitter/modulation circuitry are
operationally responsive to the MCU 1203, with an output from the
PA 1219 coupled to the duplexer 1221 or circulator or antenna
switch, as known in the art. The PA 1219 also couples to a battery
interface and power control unit 1220.
In use, a user of mobile station 1201 speaks into the microphone
1211 and his or her voice along with any detected background noise
is converted into an analog voltage. The analog voltage is then
converted into a digital signal through the Analog to Digital
Converter (ADC) 1223. The control unit 1203 routes the digital
signal into the DSP 1205 for processing therein, such as speech
encoding, channel encoding, encrypting, and interleaving. In one
embodiment, the processed voice signals are encoded, by units not
separately shown, using a cellular transmission protocol such as
global evolution (EDGE), general packet radio service (GPRS),
global system for mobile communications (GSM), Internet protocol
multimedia subsystem (IMS), universal mobile telecommunications
system (UMTS), etc., as well as any other suitable wireless medium,
e.g., microwave access (WiMAX), Long Term Evolution (LTE) networks,
code division multiple access (CDMA), wireless fidelity (WiFi),
satellite, and the like.
The encoded signals are then routed to an equalizer 1225 for
compensation of any frequency-dependent impairments that occur
during transmission though the air such as phase and amplitude
distortion. After equalizing the bit stream, the modulator 1227
combines the signal with a RF signal generated in the RF interface
1229. The modulator 1227 generates a sine wave by way of frequency
or phase modulation. In order to prepare the signal for
transmission, an up-converter 1231 combines the sine wave output
from the modulator 1227 with another sine wave generated by a
synthesizer 1233 to achieve the desired frequency of transmission.
The signal is then sent through a PA 1219 to increase the signal to
an appropriate power level. In practical systems, the PA 1219 acts
as a variable gain amplifier whose gain is controlled by the DSP
1205 from information received from a network base station. The
signal is then filtered within the duplexer 1221 and optionally
sent to an antenna coupler 1235 to match impedances to provide
maximum power transfer. Finally, the signal is transmitted via
antenna 1217 to a local base station. An automatic gain control
(AGC) can be supplied to control the gain of the final stages of
the receiver. The signals may be forwarded from there to a remote
telephone which may be another cellular telephone, other mobile
phone or a land-line connected to a Public Switched Telephone
Network (PSTN), or other telephony networks.
Voice signals transmitted to the mobile station 1201 are received
via antenna 1217 and immediately amplified by a low noise amplifier
(LNA) 1237. A down-converter 1239 lowers the carrier frequency
while the demodulator 1241 strips away the RF leaving only a
digital bit stream. The signal then goes through the equalizer 1225
and is processed by the DSP 1205. A Digital to Analog Converter
(DAC) 1243 converts the signal and the resulting output is
transmitted to the user through the speaker 1245, all under control
of a Main Control Unit (MCU) 1203--which can be implemented as a
Central Processing Unit (CPU) (not shown).
The MCU 1203 receives various signals including input signals from
the keyboard 1247. The keyboard 1247 and/or the MCU 1203 in
combination with other user input components (e.g., the microphone
1211) comprise a user interface circuitry for managing user input.
The MCU 1203 runs a user interface software to facilitate user
control of at least some functions of the mobile station 1201 to
detect joint motion using multiple sensor data. The MCU 1203 also
delivers a display command and a switch command to the display 1207
and to the speech output switching controller, respectively.
Further, the MCU 1203 exchanges information with the DSP 1205 and
can access an optionally incorporated SIM card 1249 and a memory
1251. In addition, the MCU 1203 executes various control functions
required of the station. The DSP 1205 may, depending upon the
implementation, perform any of a variety of conventional digital
processing functions on the voice signals. Additionally, DSP 1205
determines the background noise level of the local environment from
the signals detected by microphone 1211 and sets the gain of
microphone 1211 to a level selected to compensate for the natural
tendency of the user of the mobile station 1201.
The CODEC 1213 includes the ADC 1223 and DAC 1243. The memory 1251
stores various data including call incoming tone data and is
capable of storing other data including music data received via,
e.g., the global Internet. The software module could reside in RAM
memory, flash memory, registers, or any other form of writable
computer-readable storage medium known in the art including
non-transitory computer-readable storage medium. For example, the
memory device 1251 may be, but not limited to, a single memory, CD,
DVD, ROM, RAM, EEPROM, optical storage, or any other non-volatile
or non-transitory storage medium capable of storing digital
data.
An optionally incorporated SIM card 1249 carries, for instance,
important information, such as the cellular phone number, the
carrier supplying service, subscription details, and security
information. The SIM card 1249 serves primarily to identify the
mobile station 1201 on a radio network. The card 1249 also contains
a memory for storing a personal telephone number registry, text
messages, and user specific mobile station settings.
While the invention has been described in connection with a number
of embodiments and implementations, the invention is not so limited
but covers various obvious modifications and equivalent
arrangements, which fall within the purview of the appended claims.
Although features of the invention are expressed in certain
combinations among the claims, it is contemplated that these
features can be arranged in any combination and order.
* * * * *
References